BJR open最新文献

Objectives: To evaluate the effect of bowel dilation on cine-MRI small bowel motility measurements, by comparing a conventional motility score (including bowel wall and lumen) with a bowel wall-specific motility score in healthy and diseased populations.

Methods: Four populations were included: 10 Crohn's patients with a stricture and prestricture dilation for segmental motility analysis, and 14 mannitol-prepared healthy subjects, 15 fasted healthy subjects and eight chronic intestinal pseudo-obstruction (CIPO) patients (characterized by dilated bowel loops) for global small bowel motility analysis. All subjects underwent a cine-MRI scan from which two motility scores were calculated: a conventional score (including bowel wall and lumen) and a bowel wall-specific score. The difference between the two scores was calculated per population and compared between groups with a one-way ANOVA and Tukey-Kramer analysis.

Results: In Crohn's patients, the median (IQR) change between the conventional and wall-specific motility score was 0% (-2 to +4%) within the stricture and 0% (-1 to +7%) in the prestricture dilation. For the global small bowel, this was -1% (-5 to 0%) in mannitol-prepared healthy subjects, -2% (-6 to +2%) in fasted healthy subjects and +14% (+6 to+20%) in CIPO patients. The difference between the two motility scores in CIPO patients differed significantly from the four other groups (p = 0.002 to p < 0.001).

Conclusions: The conventional small bowel motility score seems robust in Crohn's disease patients and healthy subjects. In patients with globally and grossly dilated bowel loops, a bowel-wall specific motility score may give a better representation of small bowel motility.

Advances in knowledge: These findings support researchers and clinicians with making informed choices for using cine-MRI motility analysis in different populations.

Breast cancer (BC) is the most frequently diagnosed female invasive cancer in Western countries and the leading cause of cancer-related death worldwide. Nowadays, tumor heterogeneity is a well-known characteristic of BC, since it includes several nosological entities characterized by different morphologic features, clinical course and response to treatment. Thus, with the spread of molecular biology technologies and the growing knowledge of the biological processes underlying the development of BC, the importance of imaging biomarkers as non-invasive information about tissue hallmarks has progressively grown. To date, breast magnetic resonance imaging (MRI) is considered indispensable in breast imaging practice, with widely recognized indications such as BC screening in females at increased risk, locoregional staging and neoadjuvant therapy (NAT) monitoring. Moreover, breast MRI is increasingly used to assess not only the morphologic features of the pathological process but also to characterize individual phenotypes for targeted therapies, building on developments in genomics and molecular biology features. The aim of this review is to explore the role of breast multiparametric MRI in providing imaging biomarkers, leading to an improved differentiation of benign and malignant breast lesions and to a customized management of BC patients in monitoring and predicting response to treatment. Finally, we discuss how breast MRI biomarkers offer one of the most fertile ground for artificial intelligence (AI) applications. In the era of personalized medicine, with the development of omics-technologies, machine learning and big data, the role of imaging biomarkers is embracing new opportunities for BC diagnosis and treatment.

Objectives: The main objective of this work was to detect novel biomarkers in breast cancer by spreading the MR spectra over two dimensions in multiple spatial locations using an accelerated 5D EP-COSI technology.

Methods: The 5D EP-COSI data were non-uniformly undersampled with an acceleration factor of 8 and reconstructed using group sparsity-based compressed sensing reconstruction. Different metabolite and lipid ratios were then quantified and statistically analyzed for significance. Linear discriminant models based on the quantified metabolite and lipid ratios were generated. Spectroscopic images of the quantified metabolite and lipid ratios were also reconstructed.

Results: The 2D COSY spectra generated using the 5D EP-COSI technique showed differences among healthy, benign, and malignant tissues in terms of their mean values of metabolite and lipid ratios, especially the ratios of potential novel biomarkers based on unsaturated fatty acids, myo-inositol, and glycine. It is further shown the potential of choline and unsaturated lipid ratio maps, generated from the quantified COSY signals across multiple locations in the breast, to serve as complementary markers of malignancy that can be added to the multiparametric MR protocol. Discriminant models using metabolite and lipid ratios were found to be statistically significant for classifying benign and malignant tumor from healthy tissues.

Conclusions: Accelerated 5D EP-COSI technique demonstrates the potential to detect novel biomarkers such as glycine, myo-inositol, and unsaturated fatty acids in addition to commonly reported choline in breast cancer, and facilitates metabolite and lipid ratio maps which have the potential to play a significant role in breast cancer detection.

Advances in knowledge: This study presents the first evaluation of a multidimensional MR spectroscopic imaging technique for the detection of potentially novel biomarkers based on glycine, myo-inositol, and unsaturated fatty acids, in addition to commonly reported choline. Spatial mapping of choline and unsaturated fatty acid ratios with respect to water in malignant and benign breast masses are also shown. These metabolic characteristics may serve as additional biomarkers for improving the diagnostic and therapeutic evaluation of breast cancer.

Objective: To predict short-term outcomes in hospitalized COVID-19 patients using a model incorporating clinical variables with automated convolutional neural network (CNN) chest radiograph analysis.

Methods: A retrospective single center study was performed on patients consecutively admitted with COVID-19 between March 14 and April 21 2020. Demographic, clinical and laboratory data were collected, and automated CNN scoring of the admission chest radiograph was performed. The two outcomes of disease progression were intubation or death within 7 days and death within 14 days following admission. Multiple imputation was performed for missing predictor variables and, for each imputed data set, a penalized logistic regression model was constructed to identify predictors and their functional relationship to each outcome. Cross-validated area under the characteristic (AUC) curves were estimated to quantify the discriminative ability of each model.

Results: 801 patients (median age 59; interquartile range 46-73 years, 469 men) were evaluated. 36 patients were deceased and 207 were intubated at 7 days and 65 were deceased at 14 days. Cross-validated AUC values for predictive models were 0.82 (95% CI, 0.79-0.86) for death or intubation within 7 days and 0.82 (0.78-0.87) for death within 14 days. Automated CNN chest radiograph score was an important variable in predicting both outcomes.

Conclusion: Automated CNN chest radiograph analysis, in combination with clinical variables, predicts short-term intubation and death in patients hospitalized for COVID-19 infection. Chest radiograph scoring of more severe disease was associated with a greater probability of adverse short-term outcome.

Advances in knowledge: Model-based predictions of intubation and death in COVID-19 can be performed with high discriminative performance using admission clinical data and convolutional neural network-based scoring of chest radiograph severity.

Diffusion-weighted imaging is increasingly becoming popular in musculoskeletal radiology for its incremental role over conventional MR imaging in the diagnostic strategy and assessment of therapeutic response of bone and soft tissue lesions. This article discusses the technical considerations of diffusion-weighted imaging, how to optimize its performance, and outlines the role of this novel imaging in the identification and characterization of musculoskeletal lesions, such as bone and soft tissue tumors, musculoskeletal infections, arthritis, myopathy, and peripheral neuropathy. The readers can use the newly learned concepts from the presented material containing illustrated case examples to enhance their conventional musculoskeletal imaging and interventional practices and optimize patient management, their prognosis, and outcomes.

Accurate evaluation of tumor response to treatment is critical to allow personalized treatment regimens according to the predicted response and to support clinical trials investigating new therapeutic agents by providing them with an accurate response indicator. Recent advances in medical imaging, computer hardware, and machine-learning algorithms have resulted in the increased use of these tools in the field of medicine as a whole and specifically in cancer imaging for detection and characterization of malignant lesions, prognosis, and assessment of treatment response. Among the currently available imaging techniques, magnetic resonance imaging (MRI) plays an important role in the evaluation of treatment assessment of many cancers, given its superior soft-tissue contrast and its ability to allow multiplanar imaging and functional evaluation. In recent years, deep learning (DL) has become an active area of research, paving the way for computer-assisted clinical and radiological decision support. DL can uncover associations between imaging features that cannot be visually identified by the naked eye and pertinent clinical outcomes. The aim of this review is to highlight the use of DL in the evaluation of tumor response assessed on MRI. In this review, we will first provide an overview of common DL architectures used in medical imaging research in general. Then, we will review the studies to date that have applied DL to magnetic resonance imaging for the task of treatment response assessment. Finally, we will discuss the challenges and opportunities of using DL within the clinical workflow.

Objective: Imaged scan length (z-axis coverage) is a simple parameter that can reduce CT dose without compromising image quality. In CT coronary angiography (CTCA), z-axis coverage may be planned using non-contrast calcium score scan (CaCS) to identify the relevant coronary anatomy. However, standardised Agatston CaCS is acquired at 120 kV which adds a relatively high contribution to total study dose and CaCS is no longer routinely recommended in UK guidelines. We evaluate an ultra-low dose unenhanced planning scan on CTCA scan length and effective radiation dose.

Methods: An ultra-low dose tin filter (Sn-filter) planning scan (100 kVp, maximum iterative reconstruction) was performed and used to plan the z-axis coverage on 48 consecutive CTCAs (62% men, 62 ± 13 years) compared with 47 CTCA planned using a localiser alone (46% men, 59 ± 12 years) between May and June 2019. Excess scanning beyond the ideal scan length was calculated for both groups. Estimations of radiation dose were also compared between the two groups.

Results: Addition of an ultra-low dose unenhanced planning scan to CTCA protocol was associated with reduction in overscanning with no impact on image quality. There was no significant difference in total study effective dose with the addition of the planning scan, which had an average dose-length product of 3 mGy.cm. (total study dose: Protocol A 2.1 mSv vs Protocol B 2.2 mSv, p = 0.92).

Conclusion: An ultra-low dose unenhanced planning scan facilitates optimal scan length for the diagnostic CTCA, reducing overscanning and preventing incomplete cardiac imaging with no significant dose penalty or impact on image quality.

Advances in knowledge: An ultra-low dose CTCA planning is feasible and effective at optimising scan length.

Objective: The Covid-19 pandemic placed unprecedented strain on medical education and led to a vast increase in online learning. Subsequently, the Christie International Proton School moved from face-to-face to online. Delegate feedback and current literature were studied to determine benefits, challenges, and potential solutions, for online proton therapy education.

Methods: The course was converted to a 6-week online course with twice weekly 2-h sessions. Feedback was studied pre-, during-, and post-course regarding demographics, learning objectives, proton therapy knowledge, ease of engagement, technical difficulties, and course format. Statistical analyses were performed for proton therapy knowledge pre- and post-course.

Results: An increase in delegate attendance was seen with increased international and multidisciplinary diversity. Learner objectives included treatment planning, clinical applications, physics, and centre development. Average learner reported scores of confidence in proton therapy knowledge improved significantly from 3, some knowledge, to 4, adequate knowledge after the course (p<0.0001). There were minimal reported difficulties using the online platform, good reported learner engagement, and shorter twice weekly sessions were reported conducive for learning. Recordings for asynchronous learning addressed time zone difficulties.

Conclusion: The obligatory switch to online platforms has catalysed a paradigm shift towards online learning with delegates reporting educational benefit. We propose solutions to challenges of international online education, and a pedagogical model for online proton therapy education.

Advances in knowledge: Online education is an effective method to teach proton therapy to international audiences. The future of proton education includes a hybrid of online and practical face-to-face learning depending on the level of cognitive skill required.

Objectives: To assess body composition in patients with non-small cell lung cancer (NSCLC) and colorectal cancer using whole-body MRI and relate this to clinical outcomes.

Methods: 53 patients with NSCLC (28 males, 25 females; mean age 66.9) and 74 patients with colorectal cancer (42 males, 32 females; mean age 62.9) underwent staging whole-body MRI scans, which were post-processed to derive fat mass (FM), fat free mass (FFM) and skeletal muscle (SM) indices and SM fat fraction (FF). These were compared between the two cancer cohorts using two-sided t-tests and the chi-squared test. Measurements of body composition were correlated with outcomes including length of hospital stay, metastatic status and mortality.

Results: Patients with NSCLC had significantly lower FFM (p = 0.0071) and SM (p = 0.0084) indices. Mean SM FF was greater in patients with NSCLC (p = 0.0124) and was associated with longer hospital stay (p = 0.035). There was no significant relationship between FM, FFM and SM indices and length of hospital stay, metastatic status or mortality.

Conclusions: Patients with NSCLC had lower FFM and SM indices than patients with colorectal cancer and greater SMFF, indicating lower SM mass with fatty infiltration. These findings reflect differences in the phenotype of the two groups and suggest patients with lung cancer are more likely to require additional nutritional support.

Advances in knowledge: Body composition differs between NSCLC and colorectal cancer. Patients with NSCLC have both a reduced SM mass and greater SM FF suggesting that they are more nutritionally deplete than patients with colorectal cancer.